Tuesday, 11 September 2012

Business Analytics _ Session 9 & 10 _ Group G



Business Analytics _ Session 9 & 10 _ Group G
Multidimensional scaling (MDS) is often used in Marketing to identify key dimensions underlying customer evaluations of products, services or companies.
The purpose of MDS is to transform consumer judgments of similarity or preference (e.g. preference for stores or brands) into distances represented in multidimensional space. The resulting perceptual maps show the relative positioning of all objects.
Once the data is in hand, multidimensional scaling can help determine:
What dimensions respondents use when evaluating objects
How many dimensions they may use in a particular situation
The relative importance of each dimension, and
How the objects are related perceptually
Perceptual mapping is a diagrammatic technique used by asset marketers that attempts to visually display the perceptions of customers or potential customers. Typically the position of a product, product line, brand, or company is displayed relative to their competition.
Perpetual mapping software helps in a dynamically changing environment when objects in the matrix are removed or added.
Perceptual maps can have any number of dimensions but the most common is two dimensions.
Two types of Perceptual Mapping is possible
1.       Overall Similarity
2.       Attribute Based
Overall Similarity: Asking respondents about how different or how similar a pair of objects are
Advantage: Sometimes hidden attributes of an object are exposed and understood
Disadvantage: In order to make a proper interpretation complete information about the objects is required.
Attribute Based: Listing out all the attributes and ranking them in an order
Disadvantage: We may miss out mapping of some of the attributes
In Perceptual mapping 2 kinds of matrices are used
1.       Similarity matrix: When most similar object distances are taken as 1
2.       Dissimilarity matrix: When most similar object distances are taken as 0

Submitted By
Lakshmi Sravanthi
HR Batch - SIBMB

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